Big Businss Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 40.00 50.00 55.27 70.00 100.00 259
People on Welfare Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 40.00 50.00 56.73 70.00 100.00 276
Correlation between big business and people on welfare
.2249546
The correlation between the big business and people on welfare is only .2249. What this means is that the correlation of how the american public views big businesses and people on welfare is low, and that there is nothing in common between the two. (Well, it doesn't mean there is "nothing" in common between the two, it means there is a little bit of shared variation, just not that much. Right?)
Big Business Standard Deviation = 22.58
People on Welfare Standard Deviation = 21.43
For the summary of the mean and median for both big business and people on welfare the numbers were very similar. What this represents is that their is not a noticeable difference between how the american public views people on welfare and big businesses. The public feels about the same about both of these dominating groups of our society.
BOX PLOT
Both of these box plots look about the same, showing that there is no major difference in how the American public feels big business and people on welfare. Because the graphs are exactly the same it represents that there is no differing views from the american public when it comes to big business and people on welfare. (Which is which?)
SCATTER PLOT
What this scatter plot shows is the correlation and interest between how the american public feels about big business and people on welfare. The lines are steadily increasing for both big businesses and people on welfare.
VIOLIN PLOT
What this violin graphs represents is the views of the american people on big business and people on welfare. As the graph shows the american public has a slightly sharper view towards people on welfare than it does to big businesses. Even though there is a change it is a very minuscule one.
Solid work. But we will want to think extra hard about the difference between "no difference" and "only a little difference" as we move forward and use tests of significance. In effect, we need to be able to distinguish between "real" or "true" or "big" or most precisely "statistically significant" patterns and "false" or "random" or "statistically insigificant" patterns. Stay extra tuned in to this as we move forward.
Midterm
The American view on big business and people on welfare
Rdata
Big Businss
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 40.00 50.00 55.27 70.00 100.00 259
People on Welfare
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 40.00 50.00 56.73 70.00 100.00 276
Correlation between big business and people on welfare
.2249546
The correlation between the big business and people on welfare is only .2249. What this means is that the correlation of how the american public views big businesses and people on welfare is low, and that there is nothing in common between the two. (Well, it doesn't mean there is "nothing" in common between the two, it means there is a little bit of shared variation, just not that much. Right?)
Big Business Standard Deviation = 22.58
People on Welfare Standard Deviation = 21.43
For the summary of the mean and median for both big business and people on welfare the numbers were very similar. What this represents is that their is not a noticeable difference between how the american public views people on welfare and big businesses. The public feels about the same about both of these dominating groups of our society.
BOX PLOT
SCATTER PLOT
What this scatter plot shows is the correlation and interest between how the american public feels about big business and people on welfare. The lines are steadily increasing for both big businesses and people on welfare.
VIOLIN PLOT
What this violin graphs represents is the views of the american people on big business and people on welfare. As the graph shows the american public has a slightly sharper view towards people on welfare than it does to big businesses. Even though there is a change it is a very minuscule one.
Solid work. But we will want to think extra hard about the difference between "no difference" and "only a little difference" as we move forward and use tests of significance. In effect, we need to be able to distinguish between "real" or "true" or "big" or most precisely "statistically significant" patterns and "false" or "random" or "statistically insigificant" patterns. Stay extra tuned in to this as we move forward.